diff --git "a/src/medclip/test_clip.ipynb" "b/src/medclip/test_clip.ipynb" new file mode 100644--- /dev/null +++ "b/src/medclip/test_clip.ipynb" @@ -0,0 +1,429 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "kernelspec": { + "name": "python3", + "display_name": "Python 3.8.10 64-bit ('flax_p38': conda)" + }, + "interpreter": { + "hash": "ae664ea292849713b3603db15f57f78385a21cd989b61de7c26cd384959f058f" + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "import os\n", + "import jax\n", + "\n", + "from transformers import AutoTokenizer, CLIPProcessor\n", + "from configuration_hybrid_clip import HybridCLIPConfig\n", + "from modeling_hybrid_clip import FlaxHybridCLIP\n", + "from PIL import Image\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "import torchvision\n", + "from torchvision.transforms.functional import InterpolationMode\n", + "from torchvision.transforms import Resize, Normalize, ConvertImageDtype, ToTensor\n", + "import numpy as np\n", + "from run_hybrid_clip import Transform" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Unable to display metrics through TensorBoard because the package is not installed: Please run pip install tensorboard to enable.\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "# tokenizer_name = \"allenai/scibert_scivocab_uncased\"\n", + "config = HybridCLIPConfig.from_pretrained(\"../..\")\n", + "model = FlaxHybridCLIP.from_pretrained(\"../..\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 5, + "source": [ + "vision_model_name = \"openai/clip-vit-base-patch32\"\n", + "img_dir = \"/Users/kaumad/Documents/coding/hf-flax/roco-dataset/images\"\n", + "\n", + "processor = CLIPProcessor.from_pretrained(vision_model_name)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "import os\n", + "\n", + "query = 'lung'\n", + "# classes = ['brain', 'abdomen', 'breast']\n", + "# classes = ['mammography', 'CT', 'X-ray', 'ultrasound']\n", + "classes = ['man', 'woman']\n", + "# classes = ['malignancy', 'cancer', 'air']\n", + "\n", + "# img_path = os.listdir(img_dir)[0]\n", + "img_path = \"PMC4582529_JoU-2012-0014-g020.jpg\"\n", + "caption = \" Plain X-ray of the abdomen showing a well-defined, rounded soft tissue density mass, in the central abdominal region with calcification (arrows).\"\n", + "# inputs = processor(text=[query], images=None, return_tensors=\"jax\", padding=True)\n", + "# query_vec = model.get_text_features(**inputs)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "# Let's load a sample image\n", + "import os\n", + "img = Image.open(os.path.join(img_dir, img_path))\n", + "plt.imshow(img, aspect='equal')" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 239, + "source": [ + "class Transform(torch.nn.Module):\n", + " def __init__(self, image_size=224):\n", + " super().__init__()\n", + " self.transforms = torch.nn.Sequential(\n", + " Resize([image_size, image_size], interpolation=InterpolationMode.BICUBIC),\n", + " ConvertImageDtype(torch.float),\n", + " Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),\n", + " )\n", + "\n", + " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", + " with torch.no_grad():\n", + " x = self.transforms(x)\n", + " return x" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "sentences = [f'{c} with' for c in classes]\n", + "print(sentences)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['man with', 'woman with']\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 308, + "source": [ + "img_transforms = Transform()\n", + "pixel_values = img_transforms(torchvision.transforms.functional.pil_to_tensor(img)).numpy()\n", + "pixel_values = pixel_values.transpose((1, 2, 0))\n", + "pixel_values = pixel_values[np.newaxis, ...]\n", + "input_toks = tokenizer(sentences, max_length=128, padding=\"max_length\", return_tensors=\"np\",\n", + " truncation=True)\n", + "inputs = {'pixel_values': pixel_values, 'input_ids': input_toks['input_ids']}\n", + "outputs = model(**inputs)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 11, + "source": [ + "# sentences = [f'showing {c} in' for c in classes]\n", + "organs = ['breast', 'lung']\n", + "class_text = [f'{c} scan' for c in classes]\n", + "# sentences = [f'{organ} {t}' for organ in organs for t in class_text]\n", + "\n", + "# max_seq_length = 128\n", + "# pixel_values = torch.stack([example[0] for example in examples]).permute(0, 2, 3, 1).numpy()\n", + "# pixel_values = torch.stack([example[0] for example in examples]).numpy()\n", + "# inputs = tokenizer(sentences, max_length=max_seq_length, padding=\"max_length\", return_tensors=\"np\",\n", + "# truncation=True)\n", + "# batch = {\n", + "# \"pixel_values\": pixel_values,\n", + "# \"input_ids\": inputs[\"input_ids\"],\n", + "# \"attention_mask\": inputs[\"attention_mask\"],\n", + "# }\n", + "inputs = processor(text=sentences, images=img, return_tensors=\"jax\", padding=True)\n", + "inputs['pixel_values'] = inputs['pixel_values'].transpose(0, 2, 3, 1)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "source": [ + "outputs = model(**inputs)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "source": [ + "outputs.logits_per_image.shape" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(1, 2)" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 16, + "source": [ + "probs = jax.nn.softmax(outputs.logits_per_image, axis=-1)\n", + "\n", + "for c_name, prob in zip(sentences, probs[0]):\n", + " print(f'{c_name}: {prob:.2f}')" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "man with: 0.46\n", + "woman with: 0.54\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "query = 'carcinoma'\n", + "img_list = os.listdir(img_dir)[10:20]\n", + "imgs = [Image.open(os.path.join(img_dir, img_path)).convert('RGB') for img_path in img_list]\n", + "inputs = processor(text=[query], images=imgs, return_tensors=\"jax\", padding=True)\n", + "inputs['pixel_values'] = inputs['pixel_values'].transpose(0, 2, 3, 1)\n", + "outputs = model(**inputs)" + ], + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'img_dir' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/lj/gwd01lcd0h7bbvzq_1w6tdkm0000gn/T/ipykernel_86435/4273923251.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'carcinoma'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mimg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mimgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimg_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mimg_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"jax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pixel_values'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pixel_values'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'img_dir' is not defined" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 314, + "source": [ + "probs = jax.nn.softmax(outputs.logits_per_text, axis=-1)\n", + "\n", + "for img_name, prob in zip(img_list, probs[0]):\n", + " print(f'{img_name}: {prob:.2f}')" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "PMC2740232_1757-1626-0002-0000008474-003.jpg: 0.08\n", + "PMC1665243_ci05016705.jpg: 0.11\n", + "PMC3697197_JPBS-5-166-g003.jpg: 0.09\n", + "PMC4137701_CRIRA2014-614846.001.jpg: 0.10\n", + "PMC29044_cc-4-4-245-1.jpg: 0.10\n", + "PMC3789894_wjem-14-411-g001.jpg: 0.13\n", + "PMC1665243_ci05016711.jpg: 0.07\n", + "PMC4520145_dpjo-20-03-00101-gf07.jpg: 0.09\n", + "PMC1592293_1749-8090-1-29-1.jpg: 0.13\n", + "PMC4797165_13256_2016_848_Fig1_HTML.jpg: 0.09\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 213, + "source": [ + "# Encode a bunch of images using the model\n", + "embeddings = []\n", + "query = 'abdomen'\n", + "image_vec_file = './demo/image_embeddings.tsv'\n", + "\n", + "fvec = open(image_vec_file, \"w\")\n", + "img_list = os.listdir(img_dir)[:20]\n", + "\n", + "for idx, img_path in enumerate(img_list):\n", + " if idx % 20 == 0:\n", + " print(f\"{idx} images processed\")\n", + " img = Image.open(os.path.join(img_dir, img_path)).convert('RGB')\n", + " inputs = processor(images=img, return_tensors=\"jax\", padding=True)\n", + " inputs['pixel_values'] = inputs['pixel_values'].transpose(0, 2, 3, 1)\n", + " img_vec = model.get_image_features(**inputs)\n", + " img_vec = np.array(img_vec).reshape(-1)\n", + " img_vec_s = \",\".join([\"{:.7e}\".format(x) for x in img_vec])\n", + " embeddings.append(np.array(img_vec).reshape(-1))\n", + " fvec.write(f\"{img_path}\\t{img_vec_s}\\n\")\n", + "\n", + "fvec.close()\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0 images processed\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 212, + "source": [ + "def load_index(embedding_file):\n", + " filenames, image_vecs = [], []\n", + " with open(embedding_file, \"r\") as fvec:\n", + " for line in fvec:\n", + " cols = line.strip().split('\\t')\n", + " filename = cols[0]\n", + " image_vec = np.array([float(x) for x in cols[1].split(',')])\n", + " filenames.append(filename)\n", + " image_vecs.append(image_vec)\n", + " V = np.array(image_vecs)\n", + " index = nmslib.init(method='hnsw', space='cosinesimil')\n", + " index.addDataPointBatch(V)\n", + " index.createIndex({'post': 2}, print_progress=True)\n", + " return filenames, index" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 37, + "source": [ + "max_seq_length = 128\n", + "pixel_values = torch.stack([example[0] for example in examples]).permute(0, 2, 3, 1).numpy()\n", + "# pixel_values = torch.stack([example[0] for example in examples]).numpy()\n", + "captions = [example[1] for example in examples]\n", + "inputs = tokenizer(captions, max_length=max_seq_length, padding=\"max_length\", return_tensors=\"np\",\n", + " truncation=True)\n", + "batch = {\n", + " \"pixel_values\": pixel_values,\n", + " \"input_ids\": inputs[\"input_ids\"],\n", + " \"attention_mask\": inputs[\"attention_mask\"],\n", + " }\n", + "logits = model(**batch, train=False)[0]" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "torch.Size([3, 224, 224])" + ] + }, + "metadata": {}, + "execution_count": 37 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file